Index = _ball_point(ansform_coordinates(point), """ Query the kd-tree for all point within distance Return xr.DataArray(index, dims= 'pixel'), \ĭef query_ball_point( self, point, radius): Index = np.unravel_index(index, self.shape) Point - a (lat, lon) tuple or array of tuples """ Query the kd-tree for nearest neighbour. ee = spatial.cKDTree(ansform_coordinates(coords)) Y = r_n * np.cos(lat_rad) * np.sin(lon_rad)Ĭoords = np.column_stack(((), X = r_n * np.cos(lat_rad) * np.cos(lon_rad) # is coords a tuple? Convert it to an one-element array of tuples # WGS 84 reference coordinate system parametersĮ2 = 6.69437999014e-3 # eccentricity squaredĬoords = np.asarray(coords).astype(np. """ Transform coordinates from geodetic to cartesianĬoords - a set of lan/lon coordinates (e.g. """ A KD-tree implementation for fast point lookup on a 2D gridĭataset - a xarray DataArray containing lat/lon coordinatesĭef transform_coordinates( self, coords): In this way, we won’t have to transform coordinates and reconstruct the tree each time we want to look up a new set of points. For sake of efficiency, we can wrap the geodetic coordinates conversion and the KD-tree initialization in a class. We will have to make use of some NumPy acrobatics to reshape our data structures from a two-dimensional grid to a one-dimensional array, and to convert the returned one-dimensional index to a set of two indices on our original grid. In our case $n$ will be equal to the total number of ground pixels, and $m$ will be 3, as in our three dimensions $X$, $Y$, and $Z$. Once we have constructed our tree, all we have to do is to populate it with a $(n, m)$ shaped array of points and then query it the nearest neighbor to a reference point. Luckily, the SciPy library provides a very efficient KD-tree implementation so we will be spared from having to write our own. KD-trees allow to efficiently perform searches like “all points at distance lower than $r$ from $x$” or “$k$ nearest neighbors of $x$”. A common approach when it comes to finding the nearest neighbour in a number of points with $k$ dimensions is to use a KD-tree, or $k$-dimensional tree. Our problem falls into the class of nearest neighbour searches. Now that we learned a bit of MathJax and found a way to convert our pixels’ coordinates in cartesian coordinates, let’s start to think about an efficient way to compute the minimum distance between all pixels and a reference point. Temperatures = xr.DataArray(data, dims=,Ĭoords = \right] Lon, lat = np.meshgrid(np.linspace(- 20, 20, num_gp),ĭata = (np.linspace( 30, 0, num_sl*num_gp).reshape(num_sl, num_gp) + In an outburst of creativity, we could simulate some surface temperature measurements over Europe and the Mediterranean basin, on a 12x10 grid. To prototype our search algorithm, we are going to define a toy dataset, and store it into an xarray’s DataArray. Let’s get our hands dirty and start writing some code. In this post, we are going to define an algorithm to solve this problem by using coordinate transformations, $k$-dimensional trees, and xarray pointwise indexing. I have recently stumbled upon the problem of locating the closest ground pixel to a reference point, identified by its geodetic coordinates. Latitude and longitude information for each ground pixel are stored in auxiliary coordinate variables. Return ((denom!=0) ? (04) / denom : 0.The Sentinel-5P data I work on lately is typically organised on an irregular two-dimensional grid whose dimensions are scanline (along track dimension) and ground pixel (across track dimension). Var start = new Date(currentDate.getFullYear(), 0, 0) band 2 = day of the year of the pixel with max NDVI value.band 1 = max NDVI value over the time period.That said, find below the sample code, starting with “ max ndvi” script and converting it so that it returns two bands: Hi am not sure I understand the question “date might only be available for non-mosaicked images between start- and end date”.
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